United States of America buildings and infrastructure data
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City-owned and leased facilities maintained in the City's Facility System of Record (FSR). Does not include the following agencies which are not part of the City and County of San Francisco: SFHA, OCII and City College. This dataset was developed through a process involving some of the primary stewards for the facility data and their stakeholders. It is meant to be a living reference and will be maintained and updated through established workflows and processes over time. You can access the attached User Guide for more detail by clicking the red About button and scrolling to attachments. Data pushed to ArcGIS Online on October 19, 2026 at 3:15 AM by SFGIS. Data from: https://data.sfgov.org/d/nc68-ngbr Description of dataset columns: facility_id System generated unique facility identifier common_name Commonly known/used facility name address Address or location description of facility city City where facility is located. zip_code ZIP Code where facility is located. block_lot The block and lot number, also known as the Assessor Parcel Number or APN. For facilities with multiple APNs associated, the lowest numbered APN is used. This will blank if in the facility is the right-of-way or outside of San Francisco county. owned_leased Indicates if the facility is owned or leased by the City. dept_id_for_jurisdiction Department ID of the City department with jurisdiction over the facility. jurisdiction Jurisdiction: department name of the City department with jurisdiction over the facility. gross_sq_ft Gross square footage of facility (original source: FRRM). Note: Incomplete data, updating as values are determined. longitude The longitude of the facility's location. latitude The latitude of the facility's location. supervisor_district Board of Supervisors District in which the facility is located, if applicable. Note: there are city facilities that are outside the boundaries of the City and County of San Francisco. city_tenants City departments that are tenants of the facility, separated by a semi-colon. land_id Foreign key to City Lands table. geom The geometry/location column which allows the facility to be mapped in Socrata. data_last_updated Timestamp when the record (row) was last updated in the source system data_as_of Timestamp when the record (row) was last refreshed in the source system data_loaded_at Timestamp when the record (row) was was last updated here (in the data portal) Analysis Neighborhoods DELETE - Neighborhoods_from_fyvs_ahh9 DELETE - Supervisor Districts DELETE - Current Police Districts DELETE - Fire Prevention Districts DELETE - Zip Codes Neighborhoods SF Find Neighborhoods Current Police Districts Current Supervisor Districts Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.
This layer is a subset of ITSPE and and UBIDs representing vacant and blighted property building footprints. The tax assessment roll public extract (ITSPE) is used for assessment and property analysis, to send property tax bills and notices, and stores comprehensive tax information such as ownership, mailing addresses, non-contiguous Air Rights lots (Multifamily or Development), Air Rights lots, possessory interest lots, record lots, tax lots, parcels, condominiums, and federally owned lands such as reservations and appropriations. The linkage from the Vector Property layers to this database is SSL (Square, Suffix, and Lot). The UBID data was originally created by spatially joining the 2019 building footprints published in DC Open Data with the Common Ownership Lots. The UBIDs were coded using US DOE’s Implementation code. Search for UBID and ITSPE in Open Data DC for individual documentation.
Permits are how the City regulates construction. There are several different types of permits, depending on the type of construction. This table includes all permits issued through the Development Services permit center: building, mechanical plumbing, garage sales and others. Data is updated hourly during business hours. For more about permitting, please visit the Development Services website.
Fields include sites, projects, project types, notes, addresses, and geographic coordinates. This dataset will display bond projects by Charlotte-Mecklenburg schools. There are a total of 30 Bond Projects listed across the 35 Project Sites.
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized
The location of water tanks for public water systems in the Commonwealth of Kentucky. A water tank is a feature that is used for water storage in a distribution system or at a water treatment plant.
Census Current (2026) Physical Features Web Map Service; January 1, 2026 vintage
Date of Image(s): January 9, 2025, January 13, 2025, January 21, 2025 Summary: This raster dataset (TIF) represents likely damaged/destroyed urban areas in Los Angeles, California, during the fires of January 2025. The map is the result of a coherent change detection analysis of interferometric Sentinel-1 radar (InSAR) images from January 2025 in comparison with pre-disaster reference images from 2022-2025. Coherent change detection analysis is used to show change across the entire domain, which includes vegetation and forested areas. This analysis is constrained to a snapshot of the FIRIS perimeter domain to highlight the damage to built up areas only. Potentially damaged areas denoted in orange represent data collected on January 9, 2025 from a Sentinel-1 pass over the Altadena/Eaton area. Potentially damaged areas denoted in red represent data collected on January 13, 2025 from a Sentinel-1 pass over the Palisades area. Potentially damaged structures denoted in red represent data collected on January 21, 2025. Date(s) and time(s) covered: Swaths from 9 January 2025, 14 January 2025, and January 21, 2025, with 188 images collected from 2022-12-20 to 2025-01-02 for a pre-disaster reference dataset. Suggested Usage: The map is meant for landscape-level geospatial overlay and visualization purposes of fire damage in urban/built-up regions. For example, the map is useful for tabulating estimates of the number of likely damaged or destroyed structures. Pixel values of 1 represent a region likely damaged or destroyed; pixel values of 0 represent all other regions. Satellite/Sensor: European Space Agency Copernicus Sentinel-1/10 meter spatial resolution processed to 40 meters for interferometric coherence change analysis Resolution: 40 meters Credits: Damage analysis of Copernicus Sentinel-1 satellite data by Corey Scher of CUNY Graduate Center and Jamon Van Den Hoek of Oregon State University. Contact: Jamon Van Den Hoek (jamon.vandenhoek@oregonstate.edu). Data sources: Sentinel-1 https://asf.alaska.edu/datasets/daac/sentinel-1/ Alaska Satellite Facility https://hyp3-docs.asf.alaska.edu/usage_guidelines/ CA Perimeters NIFC FIRIS data (gis.data.cnra.ca.gov/datasets/CALFIRE-Forestry::ca-perimeters-nifc-firis-public-view/) Access and Use Constraints: Likely Damaged Areas: The damage map has not been field-validated and omission/commission errors are likely present. Regions that changed over the course of the reference period or were otherwise dynamic (such as places of construction or destruction or vegetated areas) are likely excluded from the analysis. Service URL: https://gis.earthdata.nasa.gov/portal/home/item.html?id=f8faeec910c74ef8a32f95838ddf0bdc WMS Endpoint: https://gis.earthdata.nasa.gov/portal/services/DISASTERS_202501_FIRE_CA/2501_sentinel1_dmg/ImageServer/WMSServer
Unified School Districts, Secondary School Districts, Elementary School Districts
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2022 vintage; Generalized
The purpose of this feature class is to illustrate the location of proposed water areas in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts. The locations of proposed water areas, along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning, project development, state and federal funding applications, Kentucky State Clearinghouse, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
The purpose of this feature class is to illustrate the location of water pumps in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts and provided by the Kentucky Division of Water. The locations of water pumps, along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning and management, project development, asset inventories, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.
This feature layer provides counts of forecasted chargers needed and current gap in charging for census tracts in Tempe. Each record represents a census tract in Tempe and includes details on forecasted charger count, existing charger, and gap in chargers at level 2 and DC Fast charging power levels. This modeled dataset was created by Catalyst Mobility (formerly Calstart). This information is a component of ongoing efforts for EV charging infrastructure development for Tempe. Data Scope Charger forecasts are to 2050 for level 2 and DC fast charging power levels. Data Use Intended to be used in the Tempe EVCI Development Strategy Map. Data Limitations Charger count data is forecasted and may not reflect actual future conditions. Data Dictionary Additional Information Contact Name: Sustainability Division Contact Email: sustainability@tempe.gov Data Source: Catalyst Mobility (formerly Calstart) Data Source Type: GIS Feature Layer Preparation Method: See Task 2 of Electric Vehicle Charging Infrastructure (EVCI) Siting Plan Assessment Publish Method: Manual Publish Frequency: One-time static
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2022 vintage; Generalized
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2023 vintage; Generalized
City facilities, public parks, public schools, libraries, parking lots and structures, fire stations, hospitals, and Woodlawn Cemetery.
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2020 vintage
The Mowing Operations dataset provides detailed information on scheduled mowing operations across District-managed properties. It includes data fields such as mowing dates, mowing frequency, and specific location identifiers like property names and addresses. The dataset also categorizes mowing areas by agency or property type—such as schools, parks, and government facilities—and includes timestamps showing the last time the information was updated. Developed to promote transparency and community engagement, the dataset allows residents to see when and where mowing services are planned throughout the season.
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
Metro Facility Benchmarking data is included in this data set including 3 elements: 1. EUI (Energy Use Intensity) values for Metro Nashville Government facilities for Calendar Year 2021 and 2022. EUI is an indicator of the amount of energy consumed on a property annually, in relation to the size of the building. It is a metric that is used to indicate if a building is energy efficient. 2. Metro facility energy consumption for Calendar Year 2021 and 2022. 3. Metro facility energy costs for Calendar Year 2021 and 2022. Source Link: https://www.nashville.gov/departments/general-servicesMetadata Document: Metro Energy Use Intensity Benchmarking Metadata.pdfContact Data Owner: opendata@nashville.gov
The purpose of this feature class is to illustrate the location of proposed water points in public water systems in the Commonwealth of Kentucky as collected by Kentucky’s Area Development Districts. The locations of proposed water points along with the basic attribute data concerning those features, are used by the Kentucky Infrastructure Authority, Area Development Districts, public water systems, and other state/local agencies for the purpose of water infrastructure planning, project development, state and federal funding applications, Kentucky State Clearinghouse, capital improvement plans, data analysis, and for any other purpose that will improve the potable water infrastructure and service in the Commonwealth of Kentucky.
Unified School Districts, Secondary School Districts, Elementary School Districts
Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows housing units in structure by tenure (owner or renter). This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized by the count and percent of housing units that are single-family detached homes. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left. Vintage: 2019-2023ACS Table(s): B25024, B25032 (Not all lines of ACS table B25032 are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases. Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.
This digital geographic dataset contains point features that represent the Building Permits issued by the City of Miami Building Department since the year 2014. The attribute table contains extensive information regarding the building permit itself, including the applications, status, cost, days, review status and many other items of interest.
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
The US Bureau of Justice Statistics describes law enforcement agencies as "publicly funded and employ at least one full-time or part-time sworn officer with general arrest powers". This dataset includes point locations for federal, state, local, and special jurisdiction law enforcement agency facilities. This dataset was created to provide a general purpose database of law enforcement facility locations. Law enforcement facility data can been used to asses whether the total police capability of a given area is adequate and to help local governments identify areas of greatest concern. The data are also a helpful resource for situational awareness planning, emergency response, and route analysis. A version of this data is also used to map Firearm Safe Harbor Storage Locations. The points in this dataset indicate the locations of law enforcement facilities (police stations, sheriff's offices, etc.) throughout the state of Utah. Each point includes attributes such as the facility name, street address, contact information, city, zip, county, and coordinates. Points have been placed on or near the main structure of the facility. UGRC originally derived this dataset from the Homeland Infrastructure Foundation-Level Data (HIFLD) database maintained by the US Bureau of Justice Statistics. The primary source for this database was the 2008 Census of State and Local Law Enforcement Agencies. The data are currently maintained by UGRC in collaboration with the Department of Public Safety's Bureau of Criminal Identification (BCI).
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2016 vintage; Generalized
Correctional facility polygons published by the US Census Bureau for the United States. Each feature represents a correctional facility and includes its name, land and water area, and centroid coordinates.
Retirement Notice: This item is in mature support as of February 2026 and will retire in December 2027. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. This layer shows tenure (owner or renter) by type of heating fuel. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant type of heating fuel mostly used in occupied housing units. Only predominant values are shown in the legend, but the count of all housing units is represented in the popup. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top left. Vintage: 2019-2023ACS Table(s): B25040, B25117 (Not all lines of ACS table B25117 are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.gov The United States Census Bureau's American Community Survey (ACS):About the Survey Geography & ACS Technical Documentation News & Updates This ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases. Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey. Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate. Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution. The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate. The estimate is controlled. A statistical test for sampling variability is not appropriate. The data for this geographic area cannot be displayed because the number of sample cases is too small.
Unified School Districts, Secondary School Districts, Elementary School Districts; January 1, 2015 vintage; Generalized